Ecology and Evolution. 2020;00:1–11.
Received: 13 Januar y 2020
Revised: 23 April 2020
Accepted: 24 April 2020
Room without a view—Den excavation in relation to body size
in brown bears
Shotaro Shiratsuru1 | Andrea Friebe2,3 | Jon E. Swenson4 | Andreas Zedrosser5,6
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2020 The Authors. Ecolog y and Evolution published by John Wiley & Sons Ltd
1Department of Biological Sciences,
University of Alberta, Edmonton, AB,
2Scandinavian Brown Bear Research Project,
3Norwegian Institute for Nature Research,
4Faculty of Environmental Sciences and
Natural Resource Management, Norwegian
University of Life Sciences, Ås, Norway
5Department of Natural Sciences and
Environmental Health, University of South-
Eastern Norway, Telemark, Norway
6Department of Integrative Biology, Institute
of Wildlife Biology and Game Management,
University of Natural Resources and Life
Sciences, Vienna, Austria
Shotaro Shiratsuru, Department of
Biological Sciences, University of Alberta,
Edmonton, AB T6G 2R3, Canada.
Andreas Zedrosser, Department of Natural
Sciences and Environmental Health,
University of South-Eastern Norway,
N-3800 Bø i Telemark, Norway.
Miljødirektoratet; Svenska Jägareförbundet;
Naturvårdsverket; Austrian Science Fund
Hibernation is an adaptive strategy to survive harsh winter conditions and food short-
age. The use of well-insulated winter dens helps animals minimize energy loss during
hibernation. Brown bears (Ursus arctos) commonly use excavated dens for hiberna-
tion. Physical attributes of excavated dens are expected to impact the bear's heat
retention and energy conservation. The objective of this study was to examine the
determinants of cavity size of excavated dens and the impact of physical attributes
of excavated dens on energy conservation in hibernating bears, hypothesizing that
bears excavate dens in a way to minimize heat loss and optimize energy conservation
during hibernation. We predicted that den cavity size would be determined by the
bear's body size and that older bears would excavate better-fitting cavities to mini-
mize heat loss, due to their previous experience. We further predicted that physical
attributes of excavated dens would affect the bears’ posthibernation body condition.
Our results revealed that bears excavated a den cavity in relation to their body size,
regardless of sex, and that older bears tended to excavate better-fitting den cavities
compared to young bears, as we expected. Older bears excavated better-fitting den
cavities, suggesting a potentially experience-based shift with age in den-excavation
behavior and an optimum cavity size relative to a bear's body size. Our key finding is
that insulation of excavated dens provided by wall/rood thickness and bedding ma-
terials had a significant positive effect on bears’ posthibernation body condition. We
believe that our study provides new insight into how not only the quality of denning
habitat, but also the quality of dens may affect hibernating animals, by presenting a
potential adaptive aspect of den preparation (age effect on efficiency in den excava-
tion) and effect of den attributes on the posthibernation body condition of brown
brown bear, den, energy conservation, hibernation, Ursus arctos
SHIRATSURU eT Al.
1 | INTRODUCTION
Hibernation is a physiological and behavioral adaptation through
which animals survive harsh seasonal conditions, such as inclement
weather or low food availability, by minimizing energy loss (Johnson
& Pelton, 1981; Pelton, Beeman, & Eagar, 1980; Thorkelson and
Maxwell 1974). Mammalian hibernators usually either hibernate
in natural cavities, such as hollow trees or natural caves, or con-
struct enclosed hibernacula, such as nests, or excavated dens or
burrows (Macartney, Larsen, & Gregory, 1989; Tietje & Ruff, 1980;
Williams & Rausch, 1973). Many hibernators also use nesting ma-
terials (e.g., grass, moss, and branches) to increase the insulation
efficiency of their hibernacula (Gedeon, Markó, Németh, Nyitrai,
& Altbäcker, 2010; Michener, 1992). Small mammalian hibernators,
such as arctic ground squirrels (Spermophilus parryii) and alpine
marmots (Marmota marmota), decrease their body temperatures to
around 0°C or even lower during hibernation to overcome their high
mass-specific metabolic rates and low amounts of body fat stores
(Geiser, 2004; Ruf & Geiser, 2015). Small hibernators also have to pe-
riodically interrupt hibernation to consume food and liquids (Carey,
Andrews, & Martin, 2003; Humphries, Thomas, & Kramer, 2001).
In comparison, large mammalian hibernators, such as brown bears
(Ursus arctos) and American black bears (Ursus americanus), decrease
metabolic rates while maintaining relatively high body tempera-
tures (Geiser, 2004; Ruf & Geiser, 2015) and rely solely on body fat
stores during hibernation (Humphries, Thomas, & Kramer, 2003).
Hibernacula that are well constructed and well insulated should help
hibernators enhance energy conservation during hibernation (Tietje
& Ruff, 1980). In addition, because ambient temperatures fluctuate
even in hibernacula, using hibernacula which provide warmer mi-
croclimates helps endothermic hibernating animals minimize energy
loss during hibernation (Boyles & McKechnie, 2010). Due to the rel-
ative lower decrease in body temperature of large hibernators, they
may be more reliant on well-constructed and well-insulated hiber-
nacula compared to small hibernators.
Brown and American black bears in several populations spend
4–6 months in winter dens (Folk, Larson, & Folk, 1976; Friebe,
Swenson, & Sandegren, 2001; Johnson & Pelton, 1981) without
eating or drinking, while using the fat storage gained during hyper-
phagia as their main energy source and conserving lean body mass
via urea recycling (Atkinson & Ramsay, 1995; Barboza, Farley, &
Robbins, 1997; Harlow, Lohuis, Grogan, & Beck, 2002; Hilderbrand
et al., 1999; Nelson, Wahner, Jones, Ellefson, & Zollman, 1973; Tøien
et al., 2011). Therefore, the use of well-insulated dens should help
bears minimize energy loss during hibernation and dens should
be selected optimally in relation to energy conservation (Hayes
& Pelton, 1994). Dens may also influence the amount of heat loss
and vulnerability to disturbances, thereby potentially affecting
the bears’ survival and reproduction (Linnell, Swenson, Andersen,
& Barnes, 2000; Manchi & Swenson, 2005; Nowack, 2015; Oli,
Jacobson, & Leopold, 1997). Enclosed dens, such as tree or rock
cavities and excavated dens, offer protection and insulation from
inclement weather (Beecham, Reynolds, & Hornocker, 1983; Oli
et al., 1997; Pelton et al., 1980; Thorkelson and Maxwell 1974). As a
bear can adjust an excavated den in relation to its body size, radiant
heat from the soil and metabolic heat from the bear can be trapped
within the den and keep the den temperature higher than the ambi-
ent temperature (Folk et al., 1976; Fornito, Lee, & Tajchman, 1982;
Vroom, Herrero, & Ogilvie, 1980). Bedding materials on the ground
may enhance insulation, by forming a microclimate between the bear
and the soil (Craighead & Craighead, 1972; Craighead, Craighead,
Varney, & Cote, 1971; Tietje & Ruff, 1980). Consequently, enclosed
dens provide bears with a microenvironment where tempera-
tures are relatively warm and stable compared to outside tem-
peratures, thereby optimizing energy conservation (Craighead &
Craighead, 1972; Tietje & Ruff, 1980). The repor ted tendency for fe-
male bears to select enclosed dens (Johnson & Pelton, 1981; Lentz,
Marchinton, & Smith, 1983; Pelton et al., 1980) may be explained by
the females’ high energy demand for birth and lactation during the
denning period (Harlow et al., 2002). However, to our knowledge,
there are no studies evaluating the potential effect of the physical
attributes of excavated dens, such as the size of the den cavity in re-
lation to an animal's body size, wall thickness, and bedding materials,
on energy loss in hibernating animals, including bears.
Worldwide, brown bears commonly use excavated dens for hi-
bernation (Craighead & Craighead, 1972; Linnell et al., 2000). Den
cavity size, composition of the wall/roof (from now on referred to
as den type), wall/roof thickness, and bedding materials have been
proposed as important factors that influence heat retention and en-
ergy conservation, thereby determining the quality of an excavated
den (Fornito et al., 1982; Pearson, 1975; Thorkelson and Maxwell
1974). A bear's body size has been suggested to affect den cavity
size (Beecham et al., 1983; Schwartz, Miller, & Franzmann, 1987;
Tietje & Ruff, 1980), but sex has not been reported to affect den
cavity size (Pigeon, Côté, & Stenhouse, 2016). The volume of the air
space between a bear and the cavity wall likely varies, with greater
air space within the den resulting in increased convective heat
loss caused by enhanced airflow (Pearson, 1975; Thorkelson and
Maxwell. 1974; Tietje & Ruff, 1980). However, an optimum size of
an air space warmed by the bear's radiative heat could contribute to
efficient heat retention (Linnell et al., 2000; Pearson, 1975). Wall/
roof thickness may be important for preserving heat within the den
(Fornito et al., 1982; Lentz et al., 1983).
In central Scandinavia, some Formica ant species build very large
mound-shaped nests, and bears often excavate abandoned “anthills”
that are overgrown by berr y bushes and use them as winter dens
(Figure 1). Anthill dens are the most common winter dens among
brown bears in central Scandinavia, utilized by 56% of females and
54% of males (Manchi & Swenson, 2005). Other den types are soil
dens, which are excavated in soil, rock cavities, or nest dens (Elfström
& Swenson, 2009). This high use of anthill dens can be explained by
the high abundance and likely also the high insulating effect of ant-
hills, and females hibernating in anthill dens tend to have a higher
reproductive success (Manchi & Swenson, 2005; Mannaart, 2016;
Nowack, 2015). Therefore, these anthill dens are suspected to pro-
vide greater energy conservation compared to other den types.
SHIR ATSURU eT Al.
In addition to the attributes of excavated dens, several other fac-
tors may affect the prehibernation body condition and energy loss
of bears during hibernation. Loss of body heat during hibernation
can be exacerbated by severe winter temperatures (Tøien, Blake, &
Barnes, 2015), even if the animal is hibernating in an enclosed cav-
ity (Thorkelson and Maxwell 1974). The amount of snow may posi-
tively affect energy conservation during hibernation by enhancing
insulation (Beecham et al., 1983; Servheen & Klaver, 1983; Sorum
et al., 2019; Vroom et al., 1980; Wathen, Johnson, & Pelton, 1986).
Energy loss and body mass in bears during hibernation are highly af-
fected by prehibernation body condition (Atkinson & Ramsay, 1995;
López-Alfaro, Robbins, Zedrosser, & Nielsen, 2013; Zedrosser,
Dahle, & Swenson, 2006). Larger bears potentially gain more fat and
lean mass prior to denning (Manchi & Swenson, 2005), considering
the positive correlation between body size and body mass (Dahle,
Zedrosser, & Swenson, 2006), and thus, males and older bears are
assumed to be in a more favorable condition at the onset of hiberna-
tion, due to their larger body size (Hilderbrand et al., 1999; Swenson,
Adamič, Huber, & Stokke, 2007). Energetic costs and weight loss
in bears increase with the duration of hibernation (López-Alfaro
et al., 2013), and the duration of denning is sex-dependent (Friebe
et al., 2001; Manchi & Swenson, 2005; Pigeon, Stenhouse, & Côté,
Here, we examined the factors affecting attributes of excavated
dens used by brown bears, testing if body size, age, and sex of bears
would affect the size of the den cavity . We hypothesized that bears
excavate dens in relation to their body size and that older and more
experienced bears excavated dens that better fit their bodies, thus
presumably being more efficient in conserving energy. We pre-
dicted that (a) cavity size would increase with body size of bears,
and (b) older bears would excavate den cavities that fit their bodies
better. We further examined whether attributes of excavated dens
affected energy conservation, hypothesizing that bears excavate
dens to minimize heat loss and optimize energy conservation during
hibernation (den attribute hypothesis). We also hypothesized that
life history (life history hypothesis) and environmental factors (envi-
ronmental variable hypothesis) affect energy conservation of bears.
These three hypotheses are not mutually exclusive, because all of
the factors (den attributes, life history of bears, and environmental
variables) can affect energy conservation of bears during hiberna-
tion. We predicted that (a) better-fitting den cavities, a higher pro-
portion of materials from anthills, and better insulation provided by
thicker wall/roof and larger bedding materials would positively af-
fect posthibernation body condition and that (b) life history (sex and
age of bears) and environmental factors (autumn food availability,
winter temperature, and snow deposition) would also affect posthi-
bernation body condition.
2 | MATERIALS AND METHODS
2.1 | Study area
The study area was in Dalarna and Gävleborg counties in south-
central Sweden (~13,000 km2, ~61N, 14E). The rolling terrain is cov-
ered by an intensively managed forest, and elevation ranges from
200 m in the southeast to 1,000 m in the west. Average temperature
is −7°C in January and 15°C in July, and snow cover generally lasts
from late October until early May. The mean annual precipitation
is 600–1,000 mm, and the vegetation period ranges from 150 to
180 days (Dahle et al., 2006). The area is mainly covered by Scots
pine (Pinus sylvestris) and Norway spruce (Picea abies) interspersed
with deciduous trees, such as mountain birch (Betula pubescens),
silver birch (Betula pendula), aspen (Populus tremula), and gray alder
FIGURE 1 A winter den of a brown
bear excavated in an abandoned anthill in
SHIRATSURU eT Al.
(Alnus incana). Ground vegetation consists of mosses, lichens, grass,
heather, and berries, including bilberries (Vaccinium myrtillus), lin-
gonberries (Vaccinium vitis-idaea), and crowberries (Empetrum her-
maphroditum), which are the main foods of bears in autumn (Stenset
et al., 2016).
Brown bears in Scandinavia hibernate in dens from late October
to late April, although males spend less time in dens than females,
and the denning duration varies in relation to age and reproductive
status in females (Friebe et al., 2001; Manchi & Swenson, 2005).
Reuse of dens by the same or different bears is very rare in brown
bears in Sweden (eight cases out of 1,091 observations during 1986–
2018; unpublished data).
2.2 | Data collection
Bears were immobilized by darting from a helicopter in spring,
shortly after den exit, and fitted with VHF (very high frequency)
radio transmitters (1985–2002) or GPS (Global Positioning
System)-GSM (Global System for Mobile Communication) col-
lars (2003–present) (Nowack, 2015; Zedrosser et al., 2006) by
the Scandinavian Brown Bear Research Project (SBBRP, www.
bearp roject.info), according to accepted veterinary and ethical
procedures. See Zedrosser et al. (2006) and Arnemo, Evans, and
Fahlman (2012) for more detailed information on capture and han-
dling. Bears were not captured before den entry, to avoid potential
Body length (cm) was measured from the tip of the nose to the
base of the tail with the tape measure overlying the dorsal midline
with the bear in sternal recumbency, and chest circumference (cm)
was measured at the widest part of the chest (Zedrosser et al., 2006).
Body mass was measured to the nearest kg with a spring scale. Ages
of bears that were not first captured as yearlings with their mothers
were estimated by counting cementum annuli in an extracted pre-
molar tooth (Matson et al., 1993). Bears captured after 5 May were
excluded from the analysis, to avoid changes in weight or body con-
dition after leaving the den, which might affect the results (Swenson
et al., 2007).
The SBBRP has collected data on winter dens from 1986 to 2016.
Winter dens were categorized into three types: anthill dens, anthill/
soil dens (20%–80% of the den material consisted of an anthill and
the rest of soil), and soil dens (>80% of the den material was soil)
(Elfström & Swenson, 2009). For each den, we recorded external size
(length × width × height) of the den (outside), as well as the size
of the den cavity, wall/roof thickness, and size of bedding materials
(length × depth). In this study, we only used data from solitary bears
that used anthill, anthill/soil, and soil dens, and that did not change
dens during the winter. We excluded the observations of totally or
partially collapsed dens from the study.
Several bears have been captured and recorded multiple times
in different years during our study. For the analyses of den cavity
size and the volume of the air space between a bear and the cav-
ity wall in relation to a bear's body size, we used the data from 86
observations of 62 solitary bears. For the analysis of posthibernation
body condition index, we used the data from 57 observations of 42
2.3 | Data preparation
We used three den types in the analysis: soil dens = 1, anthill/soil
dens = 2, and anthill dens = 3. External den size (from now on re-
ferred to as den size) and cavity size were estimated based on the
assumption that the cavity had the shape/volume of a half-dome. In
addition, we calculated indices for the average thickness of the den
wall and the size of the bed inside the den to produce an index of
insulation of the den (hereafter insulation index). Equations for each
variable are as follows:
As an index of the volume of the air space between a bear and
the cavity wall in relation to a bear's body size, we calculated the
ratio of body size to cavity size (body–cavity ratio) by estimating a
bear's body volume on the assumption that it resembles a cylinder.
Equations used for calculating the body–cavity ratio are as follows:
Because we onl y used individuals th at were captured af te r hi ber-
nation, loss of fat or body mass during hibernation could not be de-
termined. Instead, we used a posthibernation body condition index
(BCI) to evaluate the relative body condition of bears after hiberna-
tion, which can be considered as an index of energy conservation
during hibernation (Cattet, Caulkett, Obbard, & Stenhouse, 2002).
The BCI defines body condition as total body mass (kg) relative to
body size (cm) and is calculated as the standardized residual from
×inner height(m) ×
×outer height(m) ×
average of all measurements of wall and roof thickness (cm)
Insulation index =wall thickness ×bed size.
−cavity ratio =body volume(m
body length(m), and
SHIR ATSURU eT Al.
the linear regression of body mass (kg, log-transformed) against lin-
ear body length (cm, log-transformed) (Cattet et al., 2002). We con-
firmed that there is no correlation between the calculated BCI and
linear body length (r = .020, p = .873, df = 63).
We included environmental and life history variables that poten-
tially affect prehibernation body condition and energy loss during
hibernation, thereby affecting the posthibernation BCI. The environ-
mental variables were as follows: growing degree days (GDD), win-
ter severity index (WSI), and the number of snowfall days in winter.
Bears in Scandinavia rely mostly on berries, especially bilberries, for
gaining fat reserves in autumn (Dahle, Sørensen, Wedul, Swenson,
& Sandegren, 1998; Dahle et al., 2006; Hertel et al., 2018); there-
fore, berry production has an impact on the prehibernation body
condition. As an index for berry production, we used GDD > 5°C
(Hertel et al. 2018; Rixen et al. 2012). We included WSI defined as
the number of days with temperatures <−10°C from November to
April (Hertel et al. 2018) in the analysis to evaluate whether low win-
ter temperatures affect posthibernation BCI. We used the number
of days with snow on the ground in winter (hereafter annual snow
days) to examine the effect of snow deposition on posthibernation
BCI. The data on the environmental variables were obtained from
the Swedish Meteorological and Hydrological Institute (SE-601 76
Norrköping, Sweden) and were interpolated to a 1-km scale to extract
specific values for each location of a bear (Evans et al., 2017). Life his-
tory variables considered in the analyses were sex and age of bears.
2.4 | Statistical analysis
We developed statistical models based on our hypotheses and car-
ried out model selection based on Akaike's information criterion
corrected for small sample size (AICc) (Bumham & Anderson, 2002),
to obtain the most parsimonious models and parameter estimates.
When multiple candidate models showed the same level of perfor-
mance (ΔAICc < 2), we calculated model-averaged parameter esti-
mates by averaging the top models using the zero method (Grueber,
Nakagawa, Laws, & Jamieson, 2011) to examine the overall effect
size of the predictor variables.
We used generalized linear mixed-effects models (GLMMs) with
gamma distribution and a log-link function in the analysis of cavity
size, beta GLMM with logit-link function in the analysis of body–cav-
ity ratio, and linear mixed-effects models (LMMs) in the analysis of
posthibernation BCI. Because several bears were sampled multiple
times, we added individual ID as a random intercept in all analyses.
In the analysis of cavity size, we constructed candidate models with
different combinations of body length and sex (Table 1). We included
sex and age as predictor variables in candidate models in different
combinations, while controlling for body size, in the analysis of
body–cavity ratio (Table 2).
To simultaneously test our hypotheses which were not mutu-
ally exclusive, we conducted a two-step model selection approach
TABLE 1 Model selection results and parameter estimates from
the most parsimonious model in an analysis of the effect of life
history variables (sex, age, and body size) on the den cavity size (m3)
of brown bears in Sweden, 1986–2016 (n = 86 dens of 62 solitary
Models for predicting den cavit y size
Model kΔAICc w
Body length 400.693
Body length + Sex 52.20 0.231
Body length*Sex 64.40 0.077
Sex 449. 85 0.000
~1 (null) 350.21 0.000
Parameter esti mates from the most parsimonious model
interval SE t p
Body length (m) 0.83 0.64, 1.02 0.10 8.57 <.001
Note: k is the number of parameters including the intercept, ΔAICc is
the change in AICc from the most parsimonious model, and w is the
Akaike model weight. All models included individual ID as a random
intercept. β is the parameter estimate, SE is the standard error, 95% CI is
the 95% confidence interval, t is the t-value, and p is the p-value. Body
length was standardized by mean centering and dividing by two times
its standard deviation.
TABLE 2 Model selection results and parameter estimates
obtained from the most parsimonious model in an analysis of the
factors affecting body–cavity ratio of brown bears in Sweden
during 1986–2016 (n = 86 dens of 62 solitary bears)
Models for predicting body–cavit y ratio
Model kΔAICc w
Body length + Age 5 0 0.557
Body length + Sex +Age 62. 26 0.180
Body length + Sex*Age 73.28 0.10 8
Body length 43.65 0.090
~1 (null) 35.53 0.035
Body length + Sex 55.89 0.029
Parameter esti mates from the most parsimonious model
interval SE z p
Body length (m) 0.02 −0.33, 0.36 0.18 0.09 .93
Age 0.47 0.10, 0.83 0.19 2.50 .01
Note: k is the number of parameters including the intercept, ΔAICc is
the change in AICc from the most parsimonious model, and w is the
Akaike model weight. All models included individual ID as a random
intercept. β is the parameter estimate, SE is the standard error, 95% CI is
the 95% confidence interval, z is the z-value, and p is the p-value. Body
length and age were standardized by mean centering and dividing by
two times their standard deviations.
SHIRATSURU eT Al.
(Pigeon, Nielsen, Stenhouse, & Côté, 2014; Sorum et al., 2019) in
the analysis of posthibernation BCI. First, we constructed candidate
models with different combinations of predictor variables and con-
ducted model selection to obtain the best combination of predictor
variables from the most parsimonious model for each of three hy-
potheses: den attributes, life history variables, and environmental
variables hypotheses (Table 3). In a second step, we constructed
final candidate models with different combinations of the selected
predictor variables from each hypothesis and carried out model se-
lection to obtain the final most parsimonious model and parameter
All numeric predictor variables (continuous: body length, body–
cavity ratio, GDD, WSI, and annual snow days; discrete: age and
den type) were standardized by mean centering and dividing by two
times their standard deviations. The binary variable sex (male = 0
and female = 1) was centered with a mean of zero, to control for
difference in the scales and make the effect size directly comparable
to each other (Gelman, 2008). The body–cavity ratio and insulation
index were log-transformed in the analysis of posthibernation BCI
to achieve homogeneity of variance and normal distribution of re-
siduals (Zuur, Ieno, & Elphick, 2010; Zuur, Ieno, Walker, Saveliev, &
Smith, 2009). We examined multicollinearity of predictor variables
by calculating a variance inflation factor (VIF) (Zuur et al., 2010),
but no predictor variables showed VIF > 3. For linear mixed-ef-
fects models, model validation plots showed that homogeneity of
residual variance and normality of residual variance were fulfilled.
The software R 3.6.1 (R Core Team, 2019) was used for all analy-
ses. Candidate models were analyzed with the lmerTest package
(Kuznetsova, Brockhoff, & Christensen, 2017) for gamma GLMM
and LMM, and with the glmmTMB package for beta GLMM (Brooks
et al., 2017). Model selection was conducted with the MuMIn pack-
age (Barton, 2012).
3 | RESULTS
The most parsimonious model regarding the factors affecting den
cavity size only included the bears’ body length as a predictor vari-
able (Table 1), with den cavity size increasing significantly with body
length (Table 1, Figure 2a). Sex of bears was not included into the
most parsimonious model.
The most parsimonious model regarding the factors affecting
body–cavity ratio included age and body length as predictor vari-
ables, with the body–cavity ratio increasing significantly with age
when body length was controlled for (Table 2, Figure 2b).
In the first model selection step in the analysis of posthiberna-
tion BCI, insulation of dens from the den attributes hypothesis, age
of bears from the life histor y variable hypothesis, and annual snow
days from the environmental variable hypothesis were selected as
the best predictor variables for the second model selection step
(Table 3). In the second model selection step, the four top models
showed a similar level of performance (all within ΔAICc < 2) in pre-
dicting posthibernation BCI, all of which included insulation index as
a predictor variable, and the most parsimonious final model included
insulation index and age as predictor variables (Table 4). Better in-
sulation of dens positively affected posthibernation BCI (Table 4).
Older bears tended to show better posthibernation body condition,
but the effect was weak (Table 4). When we averaged the four top
models, only insulation index had a significant effect and it showed
the largest effect size (Table 4).
4 | DISCUSSION
This is the first study of determinants of den cavity size and poten-
tial effects of den attributes on posthibernation body condition of
hibernating animals, to our knowledge. We found that the cavity size
of dens excavated by brown bears was determined by a bears’ body
length, independently of sex, and that older bears excavated bet-
ter-fitting dens that are likely more energy-efficient. We also found
TABLE 3 Model selection results for different hypotheses on
the factors affecting a posthibernation body condition index of
brown bears in Sweden during 1986–2016 (n = 57 from 42 solitary
Den attributes hypothesis
Insulation 400. 51 2
Insulation + Body–cavity ratio 51.44 0.249
Insulation + Den type 52.28 0.16 4
Insulation + Den type + Body–
~1 (null) 311.85 0.001
Body–cavity ratio 4 12.81 0.001
Den type 4 13.82 0.001
Den type + Body–cavity ratio 515.17 0.000
Life history hypothesis
Sex + Age 51.40 0.239
Sex 42.68 0.126
Sex*Age 63.58 0.080
~1 (null) 33.76 0.074
Environmental variables hypothesis
~1 (null) 30.50 0.207
GDD 40.78 0.179
GDD + Snow 52.12 0.092
WSI + Snow 52.14 0.091
WSI 42.78 0.066
GDD + WSI 52.81 0.065
GDD + WSI +Snow 64.07 0.035
Note: k is the number of parameters including intercept, ΔAICc is the
change in AICc from the most parsimonious model, and w is Akaike
SHIR ATSURU eT Al.
evidence that the level of insulation of excavated dens affected the
Den cavity size was positively related to a bear's body size, as sug-
geste d , but not docum ente d, by prev ious stud ies (Beech am et al., 1983;
Schwartz et al., 1987; Tietje & Ruff, 1980). We also found that neither
cavity size nor the body–cavity ratio was affected by sex. This is con-
sist ent wit h the result s of the stu dy by Pigeon, Cô té, et al. (2016), which
reported that the cavity size of an excavated den used by brown bears
was not different between males and females. The body–cavity ratio
increased with age when body size was controlled for, which implies
that older bears excavated better-fitting den cavities.
A potential explanation for this age effect is that older bears
may be more experienced and skilled, and therefore able to exca-
vate cavities that fit their bodies better to reduce heat loss during
hibernation, compared to younger and less experienced bears.
Age and experience likely are important factors that affect ani-
mal behavior and fitness (Reiter, Panken, & Le Boeuf, 1981; Sand,
Wikenros, Wabakken, & Liberg, 2006; Sydeman, Huber, Emslie,
Ribic, & Nur, 1991). For example, previous studies have revealed that
older and experienced individuals tend to have higher reproductive
success in northern elephant seals (Mirounga angustirostris) (Reiter
et al., 1981; Sydeman et al., 1991) and reindeer (Rangifer tarandus)
(Weladji et al., 2008). Cheetahs (Acinonyx jubatus) (Eaton, 1970),
wolves (Canis lupus) (Sand et al., 2006), and polar bears (Ursus mariti-
mus) (Stirling & Latour, 1978) have been also reported to have higher
hunting success with increasing age and experience.
We found that den attributes potentially affect energy conserva-
tion in hibernating brown bears. Our results showed that wall/roof
thickness and bed size positively affected the posthibernation BCI,
partially supporting the den attributes hypothesis. These results
are consistent with previous studies that suggested that thicker
walls and bedding materials on the ground would enhance insula-
tion and heat retention for denning animals (Craighead et al., 1971;
Craighead & Craighead, 1972; Fornito et al., 1982; Lentz et al., 1983;
Linnell et al., 2000; Pearson, 1975; Thorkelson et al. 1974; Tietje &
Ruff, 1980). Boyles and McKechnie (2010) proposed that, consider-
ing that ambient temperature can vary even in hibernacula, it would
be beneficial for endothermic hibernating animals to use hibernacula
that provide them with warmer microclimates to minimize energy
loss. Our results suggest that better insulation of excavated dens,
provided by thicker walls/roofs, potentially facilitates more efficient
heat retention and energy conservation of hibernating bears. The
air space within the den also has been suggested to be an import-
ant determinant of insulation (Fornito et al., 1982; Lentz et al., 1983;
Linnell et al., 2000; Pearson, 1975), but we did not find an effect
of den cavity ratio on posthibernation BCI. We also expected that
bears hibernating in excavated dens with a higher proportion of
anthill materials would have a higher posthibernation BCI, based on
high use of anthill dens by bears and higher reproductive success of
females that hibernated in anthill dens (Manchi & Swenson, 2005;
Mannaart, 2016; Nowack, 2015). However, den type did not affect
posthibernation BCI. In addition to wall/roof thickness and bed size,
a bear's age was found to have a weak effect on posthibernation
BCI, partly supporting the life history hypothesis. We expected that
males and older bears would show higher posthibernation BCI (life
history hypothesis), because larger bears can gain more fat and lean
mass before hibernation (Dahle et al., 2006; Hilderbrand et al., 1999;
Manchi & Swenson, 2005; Swenson et al., 2007) and the females’
longer hibernation duration may result in increased energy and
weight loss (López-Alfaro et al., 2013; Manchi & Swenson, 2005).
However, our results suggest that neither sex nor age of bears had
a significant effect on posthibernation BCI, although our data did
not include females that had given birth and lactated during hiber-
nation. We also found no effect of environmental conditions on
posthibernation BCI, that is, no support for the environmental vari-
ables hypothesis. Berry production (Dahle et al., 1998, 2006; Hertel
et al., 2018) was expected to affect prehibernation body condition,
and winter temperatures (Thorkelson et al. 1974; Tøien et al., 2015)
and snow deposition (Beecham et al., 1983; Servheen and Klaver
1983; Vroom et al., 1980; Wathen et al., 1986) were expected to
FIGURE 2 Effects of life history characteristics on physical attributes of excavated winter dens of brown bears in Sweden during 1986–
2016. Predicted values by the most parsimonious models are shown with 95% confidence intervals. All the continuous predictor variables
(body length and age of bears) were back-transformed to their original means and scales. (a) Effect of body length of bears on den cavity size
of an excavated dens (n = 86 from 62 solitary bears). (b) Effect of age of bears on the body–cavity ratio of excavated dens (n = 86 from 62
solitary bears) when body length of bears is controlled for
SHIRATSURU eT Al.
affect energy loss during hibernation (Atkinson & Ramsay, 1995;
López-Alfaro et al., 2013; Zedrosser et al., 2006), thereby affect-
ing posthibernation body condition. However, neither GDD, WSI,
nor the number of snowfall days were found to be important pre-
dictors of posthibernation BCI. We used GDD as an index of pro-
duction of bilberries, which are the major food source of bears in
autumn (Dahle et al., 1998, 2006; Hertel et al., 2018), expecting that
higher GDD would positively affect prehibernation body condition
of bears. However, GDD was not selected as an important predictor
of posthibernation BCI, suggesting that the effect of berry produc-
tion on prehibernation body condition or the effect of prehiberna-
tion body condition on posthibernation body condition is probably
negligible, compared to the effect of insulation provided by an exca-
vated den, on energy conservation during hibernation. Cold winter
temperatures did not negatively affect posthibernation BCI, proba-
bly because excavated dens protected bears from the extra energy
loss caused by lower temperatures (Beecham et al., 1983; Craighead
& Craighead, 1972; Oli et al., 1997; Pelton et al., 1980; Thorkelson
& Maxwell, 1974). Lack of an effect of snow on posthibernation BCI
can be partially explained by the fact that the importance of snow
insulation likely is negligible in regions where winter temperatures
rarely drop below −20°C (Elfstrom et al. 2008; Schoen et al. 1987).
We acknowledge that the lack of data on prehibernation body
condition made it difficult to evaluate the effect of den attributes on
posthibernation body condition. In addition, additional potentially
confounding variables that we were not able to evaluate might affect
prehibernation body condition and energy conservation during hi-
bernation, such as quality of the den site in terms of topography and
insulation effects from the surrounding habitat (Sorum et al., 2019).
However, our result indicates a positive effect of insulation of ex-
cavated dens on posthibernation BCI, which suggests that physical
attributes of hibernacula potentially affect energy conservation
of hibernating mammals. Although selection of denning habitat by
animals has been thoroughly studied (Burger et al., 1988; Prior &
Weatherhead, 1996; Smereka et al., 2017; Zukal, Berková, & Řehák,
2005), the effects of hibernaculum attributes and preparation (i.e.,
bedding materials) on energy conservation during hibernation and
posthibernation body condition seem to be a knowledge gap in the
study of hibernating animals. Our study has taken a first step to fill
this gap and provides new insight into how the quality of hibernacula
may affect hibernating animals.
We are grateful to the many fieldworks and volunteers that have contrib-
uted to the data collection for this study during the course of the years.
We especially acknowledge the help of Dr. honoris causa S. Brunberg.
The long-term funding of the Scandinavian Brown Bear Research
Project (SBBRP) has come primarily from the Swedish Environmental
Protection Agency, the Norwegian Environment Agency, the Austrian
Science Fund, and the Swedish Association for Hunting and Wildlife
Management. This is paper No. 291 from the SBBRP.
CONFLICT OF INTEREST
Shotaro Shiratsuru: Conceptualization (lead); Formal analysis (lead);
Methodology (lead); Writing-original draft (lead). Andrea Friebe:
Data curation (lead); Investigation (equal); Validation (supporting);
Writing-review & editing (supporting). Jon E. Swenson: Funding
acquisition (lead); Investigation (lead); Project administration (lead);
Supervision (equal); Validation (lead); Writing-review & editing
(lead). Andreas Zedrosser: Funding acquisition (lead); Investigation
(lead); Project administration (lead); Supervision (lead); Validation
(lead); Writing-review & editing (lead).
TABLE 4 Results of the second model selection step and
parameter estimates obtained from the most parsimonious model
and model averaging of the 4 top models (ΔAICc < 2) in an analysis
of the posthibernation body condition index of brown bears in
Sweden during 1986–2016 (n = 57 from 42 solitary bears).
Final models for predicting bod y–cavity ratio
Model kΔAICc w
Insulation + Age 5 0 0.324
Insulation 40.36 0.271
Insulation + Snow 50.95 0.201
Insulation + Age + Snow 61.02 0.194
Age 48.45 0.005
Age + Snow 58.59 0.004
Snow 411.71 0.001
~1 (null) 312.21 0.001
Parameter estimates and R2 values from the most parsimonious model
interval SE t p
Insulation 0.83 0.35, 1.31 0.25 3.37 .0014
Age 0.40 −0.07, 0.89 0.25 1.64 .11
Parameter esti mates from the averaged models
SE z p
Insulation 0.89 0.39, 1.39 0.26 3.49 <.001
Age 0.16 −0.10, 0.90 0.25 0.63 .53
Snow −0.09 −0.79, 0.18 0.19 0.46 .65
Note: Insulation is an insulation index (log-transformed), and Snow is the
number of annual snow days. k is the number of parameters including
intercept, ΔAICc is the change in AICc from the most parsimonious
model, and w is Akaike model weight. All the models included individual
ID as a random intercept. β is the parameter estimate, SE is the standard
error, confidence interval is 95% confidence interval, t is the t-value, z is
the z-value, and p is the p-value. Insulation index , age, and the number
of annual snow days were standardized by mean centering and dividing
by two times their standard deviations.
SHIR ATSURU eT Al.
DATA AVA ILAB ILITY STATE MEN T
All the data used in the analysis and model validation plots are
openly available in figshare (https://usn.figsh are.com/), the data
repository of the University of South-Eastern Norway: https://doi.
org /10.23642/ usn.12174702.
Shotaro Shiratsuru https://orcid.org/0000-0001-8747-9664
Andreas Zedrosser https://orcid.org/0000-0003-4417-3037
Arnemo, J. M., Evans, A., & Fahlman, Å. (2012). Biomedical protocols for
free-ranging brown bears, gray wolves, wolverines and lynx. Evenstad,
Norway: Hedmark University College.
Atkinson, S. N., & Ramsay, M. A. (1995). The effects of prolonged fasting
of the body composition and reproductive success of female polar
bears (Ursus maritimus). Functional Ecology, 9, 559–567. https://doi.
Barboza, P. S., Farley, S. D., & Robbins, C. T. (1997). Whole-body urea
cycling and protein turnover during hyperphagia and dormancy in
growing bears (Ursus americanus and U. arctos). Canadian Journal of
Zoology, 75, 2129–2136.
Barton, K. (2012). Package ‘MuMIn’. Model selection and model averag-
ing based on information criteria. R package version1.7.11. Vienna,
Austria: R Foundation for Statistical Computing.
Beecham, J. J., Reynolds, D. G., & Hornocker, M. G. (1983). Black
bear denning activities and den characteristics in west-central
Idaho. Bears: Their Biology and Management, 5, 79–86. https://doi.
Boyles, J. G., & McKechnie, A. E. (2010). Energy conservation in hiber-
nating endotherms: Why “suboptimal” temperatures are optimal.
Ecological Modelling, 221, 164 4–16 47. https://doi.org/10.1016/j.
Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C.
W., Nielsen, A., … Bolker, B. M. (2017). glmmTMB Balances speed and
flexibility among packages for zero-inflated generalized linear mixed
modeling. The R Journal, 9, 378–400. https://doi.org/10.32614/
RJ-2 017-06 6
Bumham, K. P., & Anderson, D. R. (2002). Model selection and multimodel
inference: A practical information-theoretic approach. Berlin, Germany:
Burger, J., Zappalorti, R. T., Gochfeld, M., Boarman, W. I., Caffrey,
M., Doig, V., … Saliva, J. (1988). Hibernacula and summer den
sites of pine snakes (Pituophis melanoleucus) in the New Jersey
Pine Barrens. Journal of Herpetology, 22, 425–433. https://doi.
org /10. 2307/1564337
Carey, H. V., Andrews, M. T., & Martin, S. L. (2003). Mammalian hiberna-
tion: Cellular and molecular responses to depressed metabolism and
low temperature. Physiological Reviews, 83, 1153–1181. https://doi.
Cattet, M. R., Caulkett, N. A., Obbard, M. E., & Stenhouse, G. B. (2002).
A body-condition index for Ursids. Canadian Journal of Zoology, 80,
Craighead, F. C., & Craighead, J. J. (1972). Grizzly bear prehibernation
and denning activities as determined by radiotracking. Wildlife
Monographs, 32, 1–35.
Craighead, J. J., Craighead, F. C. Jr, Varney, J. R., & Cote, C. E. (1971).
Satellite monitoring of black bear. BioScience, 21, 1206–1212. https://
Dahle, B., Sørensen, O. J., Wedul, E. H., Swenson, J. E., & Sandegren, F.
(1998). The diet of brown bears Ursus arctos in central Scandinavia:
Effect of access to free-ranging domestic sheep Ovis aries. Wildlife
Biology, 4, 147–158.
Dahle, B., Zedrosser, A., & Swenson, J. E. (2006). Correlates with body
size and mass in yearling brown bears (Ursus arctos). Journal of Zoolog y,
269, 273–283. https://doi.org/10.1111/j.1469-7998.2006.00127.x
Eaton, R. L. (1970). Hunting behavior of the cheetah. Journal of Wildlife
Management, 34, 56–67. https://doi.org/10.2307/3799492
Elfström, M., Swenson, J. E., & Ball, J. P. (2008). Selection of denning
habitats by Scandinavian brown bears Ursus arctos. WildlifeBiology,
Elfström, M., & Swenson, J. E. (2009). Effects of sex and age on den
site use by Scandinavian brown bears. Ursus, 20, 85–94. https://doi.
Evans, A . L., Singh, N. J., Friebe, A., Arnemo, J. M., Laske, T. G., Fröber t,
O., … Blanc, S. (2017). Drivers of hibernation in the brown bear.
Frontiers in Zoology, 13, 1–7.
Folk, G. E., Larson, A., & Folk, M. A. (1976). Physiology of hibernating
bears. Bears: Their Biology and Management., 3, 373–380. https://doi.
Fornito, L., Lee, R., & Tajchman, S. J. (1982). Heat transfer models
for nesting cavities. Archives for Meteorolog y, Geophysic s, and
Bioclimatology Series B, 30, 271–282. https://doi.org/10.10 07/
Friebe, A., Swenson, J. E., & Sandegren, F. (2001). Denning chronology of
female brown bears in central Sweden. Ursus, 12, 37–45.
Gedeon, C. I., Markó, G., Németh, I., Nyitrai, V., & Altbäcker, V.
(2010). Nest material selection affects nest insulation quality for
the European ground squirrel (Spermophilus citellus). Journal of
Mammalogy, 91, 636–641.
Geiser, F. (2004). Metabolic rate and body temperature reduction during
hibernation and daily torpor. Annual Review of Physiology, 66, 239–
274. https://doi.org/10.1146/annur ev.physi ol.66.032102.115105
Gelman, A. (2008). Scaling regression inputs by dividing by two stan-
dard deviations. Statistics in Medicine, 27, 2865–2873. https://doi.
Grueber, C. E., Nakagawa, S., Laws, R. J., & Jamieson, I. G. (2011).
Multimodel inference in ecology and evolution: Challenges and
solutions. Journal of Evolutionary Biology, 24, 699–711. https://doi.
Harlow, H. J., Lohuis, T., Grogan, R . G., & Beck, T. D. (2002). Body mass
and lipid changes by hibernating reproductive and nonreproductive
black bears (Ursus americanus). Journal of Mammalogy, 83, 1020–
Hayes, S. G., & Pelton, M. R. (1994). Habitat characteristics of female
black bear dens in northwestern Arkansas. Bears: Their Biology and
Management, 9, 411–418.
Hertel, A. G., Bischof, R., Langval, O., Mysterud, A., Kindberg, J., Swenson,
J. E., & Zedrosser, A. (2018). Berry production drives bottom–up ef-
fects on body mass and reproductive success in an omnivore. Oikos,
127, 197–207. https://doi.org/10.1111/oik.04515
Hilderbrand, G. V., Schwartz, C. C., Robbins, C. T., Jacoby, M. E., Hanley,
T. A., Arthur, S. M., & Servheen, C. (1999). The importance of meat,
particularly salmon, to body size, population productivity, and
conservation of North American brown bears. Canadian Journal of
Zoology, 77, 132–138. https://doi.org/10.1139/z98-195
Humphries, M. M., Thomas, D. W., & Kramer, D. L. (2001). Torpor and
digestion in food-storing hibernators. Physiological and Biochemical
Zoology, 74, 283–292. https://doi.org/10.1086/319659
Humphries, M. M., Thomas, D. W., & Kramer, D. L. (2003). The role of
energy availability in mammalian hibernation: A cost-benefit ap-
proach. Physiological and Biochemical Zoology, 76 , 165–179. https://
SHIRATSURU eT Al.
Johnson, K. G., & Pelton, M. R. (1981). Selection and availability of dens
for black bears in Tennessee. Journal of Wildlife Management, 45, 111–
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. (2017). lmerTest
package: Tests in linear mixed effects models. Journal of Statistical
Software, 82, 1–26.
Lentz, W. M., Marchinton, R. L., & Smith, R. E. (1983). Thermodynamic
analysis of northeastern Georgia black bear dens. Journal of Wildlife
Management, 47, 545–550. https://doi.org/10.2307/3808534
Linnell, J. D., Swenson, J. E., Andersen, R., & Barnes, B. (2000). How vul-
nerable are denning bears to disturbance? Wildlife Society Bulletin,
López-Alfaro, C., Robbins, C. T., Zedrosser, A., & Nielsen, S. E. (2013).
Energetics of hibernation and reproductive trade-offs in brown
bears. Ecological Modelling, 270, 1–10. https://doi.org/10.1016/j.
Macartney, J. M., Larsen, K. W., & Gregory, P. T. (1989). Body tem-
peratures and movements of hibernating snakes (Crotalus and
Thamnophis) and thermal gradients of natural hibernacula. Canadian
Journal of Zoology, 67(1), 108–114. https://doi.org/10.1139/z89-017
Manchi, S., & Swenson, J. E. (2005). Denning behaviour of Scandinavian
brown bears Ursus arctos. Wildlife Biology, 11, 123–133. https://doi.
Mannaart, A. H. I. (2016). Denning ecology of Scandinavian brown bears
(Ursus arctos) in a dynamic landscape. Master thesis. Norwegian
University of Life Sciences, Ås.
Matson, G. M. L. J., Van Daele, L., Goodwin, E., Aumiller, L., Reynolds, H.,
& Hristienko, H. (1993). A laboratory manu al for cementum age d etermi-
nation of Alaska brown bear first premolar teeth. Matson’s Laboratory.
Michener, G. R. (1992). Sexual differences in over-winter torpor patterns
of Richardson's ground squirrels in natural hibernacula. Oecologia, 89,
397–406. https://doi.org/10.1007/BF003 17418
Nelson, R. A ., Wahner, H. W., Jones, J. D., Ellefson, R. D., & Zollman,
P. E. (1973). Metabolism of bears before, during, and after winter
sleep. American Journal of Physiology, 224, 491–496. https://doi.
Nowack, L. (2015). Reproductive performance of Scandinavian female
brown bears (Ursus arctos) in relation to the use of den-type. Master
thesis. University of Natural Resources and Applied Life Sciences,
Oli, M. K., Jacobson, H. A., & Leopold, B. D. (1997). Denning ecology of
black bears in the White River National Wildlife Refuge, Arkansas.
The Journal of Wildlife Management, 61, 700–706. https://doi.
org /10. 2307/3802177
Pearson, A. M. (1975). The northern interior grizzly b ear, Ursus arctos L.
Canadian Wildlife Service Report Series. 34.
Pelton, M. R., Beeman, L. E., & Eagar, D. C. (1980). Den selection by black
bears in the Great Smoky Mountains National Park. Bears: Their Bi ology
and Management, 4, 149–151. https://doi.org/10.2307/3872859
Pigeon, K. E., Côté, S. D., & Stenhouse, G. B. (2016). Assessing den
selection and den characteristics of grizzly bears. The Journal of
Wildlife Management, 80(5), 884–893. https://doi.org/10.1002/
Pige on, K. E., Niel sen , S. E., Sten hou se, G. B., & Côté, S. D. (2014). Den se-
lection by grizzly bears on a managed landscape. Journal of Mammalogy,
95, 559–571. https://doi.org/10.1644/13-MAMM-A-137
Pigeon, K. E., Stenhouse, G., & Côté, S. D. (2016). Drivers of hiberna-
tion: Linking food and weather to denning behaviour of grizzly bears.
Behavioral Ecology and Sociobiology, 70, 1745–1754. https://doi.
Prior, K. A., & Weatherhead, P. J. (1996). Habitat features of black rat
snake hibernacula in Ontario. Journal of Herpetology, 30, 211–218.
htt ps://doi.org /10.23 07/1565512
R Core Team (2019). A language and environment for statistical computing.
Vienna, Austria: R Foundation for Statistical Computing.
Reiter, J., Panken, K. J., & Le Boeuf, B. J. (1981). Female competition and
reproductive success in northern elephant seals. Animal Behavior, 29,
Rixen, C ., Dawes, M. A., Wipf, S., & Hagedorn, F. (2012). Evidence of en-
hanced freezing damage in treeline plants during six years of CO2
enrichment and soil warming. Oikos, 121(10), 1532–1543.
Ruf, T., & Geiser, F. (2015). Daily torpor and hibernation in birds and
mammals. Biological Reviews, 90, 891–926 . https://doi.or g/10.1111/
Sa nd, H., Wikenro s, C., Waba kken, P., & Liber g, O. (2006). Ef fec ts of h unt-
ing group size, snow depth and age on the success of wolves hunt-
ing moose. Animal Behavior, 72, 781–789. https://doi.org/10.1016/j.
Schoen, J. W., Beier, L. R., Lentfer, J. W., & Johnson, L. J. (1987). Denning
ecology of brown bears on Admiralty and Chichagof Islands. Bears:
Their Biology and Management, 293–304.
Schwartz, C. C., Miller, S. D., & Franzmann, A. W. (1987). Denning ecol-
ogy of three black bear populations in Alaska. Bears: Their Biology and
Management, 7, 281–291. https://doi.org/10.2307/3872635
Servheen, C., & Klaver, R. (1983). Grizzly bear dens and denning activity in
the Mission and Rattlesnake Mountains. Montana. Bears: Their Biology
and Management, 5, 201–207. https://doi.org/10.2307/3872539
Smereka, C. A., Edwards, M. A., Pongracz, J., Branigan, M., Pilfold, N.
W., & Derocher, A . E. (2017). Den selection by barren-ground griz-
zly bears, Mackenzie Delta, Northwest Territories. Polar Biology, 40,
Sorum, M. S., Joly, K., Wells, A. G., Cameron, M. D., Hilderbrand, G. V.,
& Gustine, D. D. (2019). Den-site characteristics and selection by
brown bears (Ursus arctos) in the central Brooks Range of Alaska.
Ecosphere, 10, e02822.
Stenset, N. E., Lutnæs, P. N., Bjarnadóttir, V., Dahle, B., Fossum, K. H.,
Jigsved, P., … Swenson, J. E. (2016). Seasonal and annual variation in
the diet of brown bears Ursus arctos in the boreal forest of southcen-
tral Sweden. Wildlife Biology, 22, 107–117.
Stirling, I., & Latour, P. B. (1978). Comparative hunting abilities of polar
bear cubs of different ages. Canadian Journal of Zoology, 56, 1768–
Swenson, J. E., Adamič, M., Huber, D., & Stokke, S. (2007). Brown bear
body mass and growth in northern and southern Europe. Oecologia,
153, 37–47. https://doi.org/10.1007/s00442-0 07-0715-1
Sydeman, W. J., Huber, H. R., Emslie, S. D., Ribic, C. A., & Nur, N. (1991).
Age-specific weaning success of northern elephant seals in relation
to previous breeding experience. Ecology, 72, 2204–2217. https://
Thorkelson, J., & Maxwell, R. K. (1974). Design and testing of a heat
transfer model of a raccoon (Procyon lotor) in a closed tree den.
Ecology, 55, 29–39.
Tietje, W. D., & Ruff, R. L. (1980). Denning behavior of black bears in bo-
real forest of Alberta. Journal of Wildlife Management, 44, 858–870.
Tøien, Ø., Blake, J., & Barnes, B. M. (2015). Thermoregulation and ener-
getics in hibernating black bears: Metabolic rate and the mystery of
multi-day body temperature cycles. Journal of C omparative Physiolog y
B, 185, 447–461.
Tøien, Ø., Blake, J., Edgar, D. M., Grahn, D. A., Heller, H. C., & Barnes,
B. M. (2011). Hibernation in black bears: Independence of metabolic
suppression from body temperature. Science, 331, 906–909. https://
Vroom, G. W., Herrero, S., & Ogilvie, R. T. (1980). The ecology of win-
ter den sites of grizzly bears in Banff National Park, Alberta.
Bears: Their Biology and Management, 4, 321–330. https://doi.
Wathen, W. G., Johnson, K. G., & Pelton, M. R. (1986). Characteristics of
black bear dens in the southern Appalachian region. Bears: Their Bi ology
and Management, 6, 119–127. https://doi.org/10.2307/3872815
SHIR ATSURU eT Al.
Weladji, R. B., Loison, A., Gaillard, J. M., Holand, Ø., Mysterud, A., Yoccoz,
N. G., … Stenseth, N. C. (2008). Heterogeneity in individual quality
overrides costs of reproduction in female reindeer. Oecologia, 156,
Williams, D. D., & Rausch, R . L. (1973). Seasonal carbon dioxide and ox-
ygen concentrations in the dens of hibernating mammals (Sciuridae).
Comparative Biochemistry and Physiology, 44(4), 1227–1235. https://
Zedrosser, A., Dahle, B., & Swenson, J. E. (2006). Population den-
sity and food conditions determine adult female body size in
brown bears. Journal of Mammalogy, 87, 510 –518. ht tps: //do i.
Zukal, J., Berková, H., & Řehák, Z. (2005). Activity and shelter selection
by Myotis myotis and Rhinolophus hipposideros hibernating in the
Kateřinská cave (Czech Republic). Mammalian Biology, 70, 271–281.
Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration
to avoid common statistical problems. Methods i n Ecology a nd Evolution,
1, 3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x
Zuur, A ., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009).
Mixed eff ects models and e xtensions in ecolo gy with R. Berlin, Germany:
How to cite this article: Shiratsuru S, Friebe A, Swenson JE,
Zedrosser A. Room without a view—Den excavation in
relation to body size in brown bears. Ecol Evol. 2020;00:1–11.